Accordant is a .NET framework that turns executable behavioral specifications into generated tests for checking software implementations. It helps developers verify that real systems follow defined rules across many operations and state changes.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add microsoft/accordant --skill stategit clone --depth 1 https://github.com/microsoft/accordantWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/microsoft/accordant/state)<a href="https://agentmods.dev/skills/microsoft/accordant/state"><img src="https://agentmods.dev/badge/skills/microsoft/accordant/state/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/microsoft/accordant/state"><img src="https://agentmods.dev/badge/skills/microsoft/accordant/state.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.01268 |
| Opus 5.5 | $0.00010 | $0.00507 |
| Sonnet 5.5 | $0.00005 | $0.00254 |
| Haiku 4.5 | $0.00002 | $0.00127 |
Grade A, and why
accordant-state scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designing State in Accordant
State is what an external observer needs to know to predict what an operation should return. Keep it minimal — only track what's necessary to define correct behavior.
The [State] Attribute
Use [State] on a partial class to get automatic cloning, equality, and hashing:
[State]
public partial class BankState
{
public Dictionary<string, decimal> Accounts { get; set; } = new();
}
The source generator handles:
- Deep cloning for immutable state transitions
- Equality comparison for state deduplication
- Hashing for state graph exploration
State Design Principles
Keep It Minimal
Ask: "Does any operation need this to determine its response?" If no, leave it out.
// ❌ Too detailed - includes implementation concerns
[State]
public partial class BadState
{
public Dictionary<string, AccountEntity> Accounts { get; set; } // EF entities
public DateTime LastModified { get; set; } // Not needed for behavior
public string ConnectionString { get; set; } // Implementation detail
}
// ✅ Just what operations need
[State]
public partial class GoodState
{
public Dictionary<string, decimal> Accounts { get; set; } = new(); // Just balances
}
Nested State Classes
For complex domains, nest state classes:
[State]
public partial class AppState
{
public Dictionary<string, UserState> Users { get; set; } = new();
}
[State]
public partial class UserState
{
public string Name { get; set; } = string.Empty;
public Dictionary<string, TodoState> Todos { get; set; } = new();
}
[State]
public partial class TodoState
{
public string Title { get; set; } = string.Empty;
public bool Completed { get; set; } = false;
}
State Transitions
Operations return expected outcomes that describe both the response and the next state.
No State Change: .SameState()
For errors or read-only operations:
// Error - doesn't modify state
if (!state.Accounts.ContainsKey(accountId))
return Expect.That<ApiResult<decimal>>(r => r.IsNotFound).SameState();
// Read-only operation
return Expect.That<ApiResult<decimal>>(r => r.IsSuccess && r.Data == balance).SameState();
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 193 lines · 24 tokens per session scan A 051cb1258279
accordant-state is a skill published in the GitHub repository microsoft/accordant (69 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 1,268 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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